In Nonparametric and High-Dimensional Models, Bayesian Ignorability is an Informative Prior
非参数与高维模型中的贝叶斯可忽略性是一种信息性先验AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要聚焦缺失数据机制与贝叶斯先验，未明确给出因果效应目标或其他所列细分应用。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
非参数与高维模型中的贝叶斯可忽略性是一种信息性先验
- 研究问题
- 在缺失数据和高维干扰参数并存时，贝叶斯可忽略性会如何隐含限制选择偏差先验，并影响对混杂偏差的判断？
- 方法与思路
- 作者分析可忽略性对选择偏差先验的约束，以多个模型展示，并在岭回归先验的高维线性模型中刻画后验影响。
- 材料陈述的贡献
- 摘要称许多问题中可忽略性使选择偏差先验紧密集中于零，并讨论如何表达合理偏差信念及该问题较轻的狭窄情形。
- 阅读关注点
- 应核对贝叶斯可忽略性的精确定义、先验独立性与维数的作用，以及偏差先验集中结论和例外情形的条件。
- 阅读边界
- 仅据版本未注明的 OpenAlex 摘要；不能据此断言所有可忽略性分析均有问题，具体模型和例外条件未提供，也不能推断刊本变化。
原文摘要
In problems with large amounts of missing data one must model two distinct data generating processes: the outcome process, which generates the response, and the missing data mechanism, which determines the data we observe. Under the ignorability condition of Rubin, however, likelihood-based inference for the outcome process does not depend on the missing data mechanism so that only the former needs to be estimated; partially because of this simplification, ignorability is often used as a baseline assumption. We study the implications of Bayesian ignorability in the presence of high-dimensional nuisance parameters and argue that ignorability is typically incompatible with sensible prior beliefs about the amount of confounding bias. We show that, for many problems, ignorability directly implies that the prior on the selection bias is tightly concentrated around zero. This is demonstrated on several models of practical interest, and the effect of ignorability on the posterior distribution is characterized for high-dimensional linear models with a ridge regression prior. We then show both how to build high-dimensional models that encode sensible beliefs about the confounding bias and also show that under certain narrow circumstances ignorability is less problematic. Supplementary materials for this article are available online.